Probability density function (PDF) estimation is a very critical task in many applications of data analysis. For example in the Bayesian framework decisions are taken according to Bayes' rule, which directly involves the evaluation of the PDF. Many methods are available to this aim, but there is no consensus in the literature about which to use, nor about the pros and cons of each of them. In this paper, we present a thorough and extensive experimental comparison between two of the most popular methods: Parzen window and finite Gaussian mixture. Extended experimental results and application development guidelines are reported.